From PDCN, we have got a pyramid of warped features. In this part, we employ MFSN to fuse the multi-level warped features and reconstruct the final results. GridNet [1]} has shown its effectiveness on frame synthesis in various VFI methods [2]}, [3]}. The original version of GridNet has single input and single output, here we design a modified version that has multi-level inputs and reconstruct multi-level outputs simultaneously. Its structure is shown in Fig. REF . The network employs the pyramid intermediate features \(F_t^l=[F_{0\rightarrow t}^l, F_{1\rightarrow t}^l]\)  as inputs and reconstructs the image residuals \(\Delta I_t^l\)  after the rightmost lateral block. Thus, the final interpolated intermediate frame in the \(l\) -th level can be obtained by:
\(I_t^l = \overline{I}_t^l + \Delta I_t^l,\) 
